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<@ULVA73B9P> I have added this to my job_variables...
# ask-marvin
a
@Marvin I have added this to my job_variables so why am I getting Finished in state Failed('Flow run encountered an exception. ScriptError: Script at \'dna/orchestration/prefect/flows/industry_searched_training.py\' encountered an exception: ModuleNotFoundError("No module named \'boto3\'")') {env: {"EXTRA_PIP_PACKAGES": "sagemaker boto3"}}
m
It seems you're encountering a 
ModuleNotFoundError
due to 
boto3
not being installed, despite setting the 
EXTRA_PIP_PACKAGES
environment variable. Here's a step-by-step plan to resolve this: 1. Ensure the Syntax of 
EXTRA_PIP_PACKAGES
: Verify that you’ve correctly set the 
EXTRA_PIP_PACKAGES
environment variable. Prefect should install those packages inside your deployment environment. 2. Check Environment Configuration: Your environment variable setup should look similar to:
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python
   job_variables = {
       "env": {
           "EXTRA_PIP_PACKAGES": "sagemaker boto3"
       }
   }
Make sure this is correctly set within your deployment script. 3. Debug the Docker Image: If you're using a custom Docker image, ensure 
boto3
is included in the requirements. Your Dockerfile should have:
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dockerfile
   FROM prefecthq/prefect:latest
   RUN pip install sagemaker boto3
4. Check Prefect Logs: Review the logs from your flow run to see if there are any messages about failing to install the packages. 5. Manual Verification: Sometimes manually checking if the package is in the container can help:
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sh
   docker run -it prefecthq/prefect:latest python -m pip show boto3
6. Issues and Documentation: - Documentation on adding extra packages with `EXTRA_PIP_PACKAGES`: Add Pip Packages. - Similar issues: - Missing dependencies issue (GitHub Issue #6219) - Image missing extras (GitHub Issue #5510) By ensuring your deployment is correctly setting the 
EXTRA_PIP_PACKAGES
and confirming the packages in your Docker images, you should be able to resolve the 
ModuleNotFoundError
with 
boto3
. Feel free to ask for more details or troubleshooting steps!
a
@Marvin In my prefect.yaml file work_pool: name: nice-workpool work_queue_name: job_variables: {env: {"not_secret_secret": '{{ prefect.blocks.secret.not-so-secret-1 }}'}, "not_secret_secret_2": '{{ prefect.blocks.secret.not-so-secret-2 }}', "EXTRA_PIP_PACKAGES": "sagemaker boto3"}
m
Your 
prefect.yaml
setup for 
job_variables
looks mostly correct, but to ensure that 
EXTRA_PIP_PACKAGES
is properly set and that your secrets are being fetched correctly, it might help to conform to Prefect's expected yaml structure and syntax closely. Here's how you can make sure everything is set up correctly: 1. Check and Correct YAML Formatting: Ensure that the YAML is syntactically correct and aligns with what Prefect expects:
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yaml
work_pool:
  name: nice-workpool
  work_queue_name:
  job_variables:
    env:
      not_secret_secret: '{{ prefect.blocks.secret.not-so-secret-1 }}'
      not_secret_secret_2: '{{ prefect.blocks.secret.not-so-secret-2 }}'
      EXTRA_PIP_PACKAGES: 'sagemaker boto3'
2. Verify Blocks: Make sure the secrets 
not-so-secret-1
and 
not-so-secret-2
exist and are correctly set up in the Prefect UI or via CLI. 3. Deployment Setup: If using Prefect CLI to create the deployment, make sure it references this YAML file:
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sh
prefect deployment build --name my_deployment --work-pool nice-workpool -q nice-workpool-queue my_flow.py:flow -o prefect.yaml
4. Logs and Validation: - Inspect Logs: When you run a flow, inspect the logs to ensure that the 
EXTRA_PIP_PACKAGES
environment variable is working correctly. - Check Image Packages: Verify if the docker image properly installs the packages listed in 
EXTRA_PIP_PACKAGES
. 5. Manual Secret Check: If 
prefect.blocks.secret.not-so-secret-1
and 
prefect.blocks.secret.not-so-secret-2
return correctly outside the deployment process, they should similarly work within 
job_variables
. Here’s a deployment example to test:
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python
from prefect import flow, get_run_logger

@flow
def my_flow():
    logger = get_run_logger()
    <http://logger.info|logger.info>("Running flow...")
    import boto3  # Attempt to import boto3 to verify installation
    <http://logger.info|logger.info>("boto3 imported successfully!")


if __name__ == "__main__":
    my_flow()
Deploy and run this flow using the deployment:
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sh
prefect deployment apply prefect.yaml
prefect deploy my_flow
This should confirm if environment variables including 
EXTRA_PIP_PACKAGES
are working correctly within your Prefect deployment. For further reading and potential issues, you can refer to: - Prefect deployments documentation: Prefect-deploy - Related Prefect GitHub Issues GitHub Issue #6219 and GitHub Issue #5510. If you need any more guidance, feel free to ask!
a
@Marvin With a prefect serveless aws push workpool how can I get cloudwatch logs if I use the provision infra option
m
Configuring CloudWatch Logs for Serverless AWS Push Workpool with Prefect and Provisioned Infrastructure To get CloudWatch logs when using a serverless AWS push workpool and the 
--provision-infra
option, you need to ensure that your AWS permissions and configurations are properly set up. Here's how to proceed: 1. Ensure AWS CLI Setup: - Install the AWS CLI and authenticate with your AWS account. - Ensure you have the necessary permissions: - IAM permissions: 
iam:CreatePolicy
, 
iam:GetPolicy
, 
iam:ListPolicies
, 
iam:CreateUser
, 
iam:GetUser
, 
iam:AttachUserPolicy
, 
iam:CreateRole
, 
iam:GetRole
, 
iam:AttachRolePolicy
, 
iam:ListRoles
, 
iam:PassRole
- ECS permissions: 
ecs:CreateCluster
, 
ecs:DescribeClusters
- EC2 permissions: 
ec2:CreateVpc
, 
ec2:DescribeVpcs
, 
ec2:CreateInternetGateway
, 
ec2:AttachInternetGateway
, 
ec2:CreateRouteTable
, 
ec2:CreateRoute
, 
ec2:CreateSecurityGroup
, 
ec2:DescribeSubnets
, 
ec2:CreateSubnet
, 
ec2:DescribeAvailabilityZones
2. Provision Infrastructure with CloudWatch Enabled: Issue the following command to create a work pool with infrastructure provisioning:
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sh
   prefect work-pool create --name nice-workpool --type aws --provision-infra --extra-pip-packages "sagemaker boto3"
This will set up the necessary AWS infrastructure automatically. 3. Enable CloudWatch Logging: Make sure your ECS tasks are configured to send logs to CloudWatch. Modify the ECS task definition to include log configuration:
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json
   {
       ...
       "logConfiguration": {
           "logDriver": "awslogs",
           "options": {
               "awslogs-region": "us-west-2",
               "awslogs-group": "/ecs/your-task-name",
               "awslogs-stream-prefix": "ecs"
           }
       }
       ...
   }
4. Verifying Logs in CloudWatch: Navigate to the CloudWatch Logs in your AWS Console: - Go to the 
Logs
section. - Find your log group using the specified 
awslogs-group
. - Review the streaming logs. For additional guidance, please refer to: - Prefect Push Work Pools - AWS Documentation on Using CloudWatch Logs with ECS You can find information on setting the 
--provision-infra
flag and logs configuration in the Prefect documentation and AWS guides. If any issues arise, feel free to ask for more specific assistance!
a
@Marvin Unexpected keys: [logConfiguration].
m
The 
logConfiguration
key needs to be included in the 
containerDefinitions
part of your AWS ECS task definition. Here’s how you can modify it: 1. Task Definition JSON: Make sure your 
logConfiguration
is correctly nested within the 
containerDefinitions
in your ECS task definition.
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json
{
  "family": "your-task-family",
  "taskRoleArn": "arn:aws:iam::your-account-id:role/your-task-role",
  "executionRoleArn": "arn:aws:iam::your-account-id:role/your-execution-role",
  "networkMode": "awsvpc",
  "containerDefinitions": [
    {
      "name": "your-container-name",
      "image": "your-image:tag",
      "memory": 512,
      "cpu": 256,
      "essential": true,
      "logConfiguration": {
        "logDriver": "awslogs",
        "options": {
          "awslogs-group": "/ecs/your-log-group",
          "awslogs-region": "your-region",
          "awslogs-stream-prefix": "ecs"
        }
      },
      "environment": [
        {
          "name": "EXTRA_PIP_PACKAGES",
          "value": "sagemaker boto3"
        }
      ]
    }
  ],
  "requiresCompatibilities": [
    "FARGATE"
  ],
  "cpu": "256",
  "memory": "512"
}
2. Update Task Definition: Update your ECS task definition with this configuration. You can do this in the AWS Console or using the AWS CLI:
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sh
   aws ecs register-task-definition --cli-input-json <file://path_to_your_task_definition.json>
3. Prefect Deployment Configuration: Make sure your deployment YAML for Prefect references this ECS task definition appropriately and includes necessary environment variables. 4. Provisioning Infrastructure with Prefect: When creating your Prefect work pool, ensure the infrastructure is correctly set:
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sh
   prefect work-pool create --name nice-workpool --type aws --provision-infra
5. Review (Expected): By including the correct 
logConfiguration
in 
containerDefinitions
, your ECS tasks should now send logs to CloudWatch. Check the CloudWatch logs at: - AWS Management Console -> CloudWatch -> Logs -> Log Groups (/ecs/your-log-group) Refer to AWS ECS documentation for further details on logging: Using CloudWatch Logs with Amazon ECS. If you face further issues or have configuration-specific questions, feel free to ask!
a
@Marvin it looks like in the prefect workpool task defintion that these variables can be used how can I add these to the prefect workpool "configure_cloudwatch_logs": "{{ configure_cloudwatch_logs }}", "cloudwatch_logs_options": "{{ cloudwatch_logs_options }}",
m
To configure CloudWatch logs in your Prefect AWS work pool, you can utilize the 
configure_cloudwatch_logs
and 
cloudwatch_logs_options
keys in the task definition. Here’s how you can do this: 1. Update Prefect Work Pool Configuration: You need to include the 
configure_cloudwatch_logs
and 
cloudwatch_logs_options
settings in your 
prefect.yaml
or when creating the work pool. 2. Example Configuration: Here’s an example of how you can configure these options within the `prefect.yaml`:
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yaml
work_pool:
  name: nice-workpool
  type: ecs
  job_variables:
    configure_cloudwatch_logs: true
    cloudwatch_logs_options:
      awslogs-group: "your-log-group"
      awslogs-region: "your-region"
      awslogs-stream-prefix: "ecs"
    env:
      not_secret_secret: '{{ prefect.blocks.secret.not-so-secret-1 }}'
      not_secret_secret_2: '{{ prefect.blocks.secret.not-so-secret-2 }}'
      EXTRA_PIP_PACKAGES: "sagemaker boto3"
3. Apply the Work Pool Configuration: Ensure you apply this configuration when creating the work pool:
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sh
   prefect work-pool create --name nice-workpool --type ecs --job-variables-from-file prefect.yaml
4. Verifying Logs: Check the CloudWatch logs in the AWS Management Console to ensure that logs are being streamed correctly: - Go to CloudWatch -> Logs -> Log Groups - Locate the log group 
/ecs/your-log-group
- View the logs under the specified 
awslogs-stream-prefix
. 5. AWS Permissions: Ensure the necessary permissions for CloudWatch logging are configured in your AWS IAM roles: - 
logs:CreateLogStream
- 
logs:PutLogEvents
For more detailed information, refer to: - Prefect AWS ECS Integration: Prefect AWS ECS Integration - AWS CloudWatch Logs Configuration: AWS CloudWatch Logs By following these steps and using the example configuration, you should be able to enable CloudWatch logs for your Prefect AWS serverless work pool.
a
@Marvin thanks for your help I have decided that instead I should extend the latest prefect image and add the pip packages I need and deploy this to ECR
m
That sounds like a solid approach! Extending the Prefect image and adding your required pip packages will give you more control and ensure that all dependencies are included. Here’s a step-by-step guide: 1. Create a Dockerfile: Create a Dockerfile to extend the latest Prefect image and add your pip packages.
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Dockerfile
# Use the latest Prefect image as the base
FROM prefecthq/prefect:latest-python3.10

# Install necessary pip packages
RUN pip install sagemaker boto3

# Add any additional dependencies or configurations as needed
# e.g., ENV PREFECT__LOGGING__LEVEL=DEBUG

# Command to run the Prefect agent (or any other command)
CMD ["prefect", "agent", "start"]
2. Build the Docker Image: Build the Docker image locally.
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sh
docker build -t my-prefect-image:latest .
3. Tag the Docker Image for ECR: Tag the image to match your ECR repository.
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sh
aws_account_id=your_aws_account_id
region=your_aws_region
ecr_repo_name=your_ecr_repo_name

docker tag my-prefect-image:latest $aws_account_id.dkr.ecr.$<http://region.amazonaws.com/$ecr_repo_name:latest|region.amazonaws.com/$ecr_repo_name:latest>
4. Login to Amazon ECR: Authenticate Docker to your Amazon ECR registry.
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sh
aws ecr get-login-password --region $region | docker login --username AWS --password-stdin $aws_account_id.dkr.ecr.$<http://region.amazonaws.com|region.amazonaws.com>
5. Push the Docker Image to ECR: Push the tagged image to your ECR repository.
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sh
docker push $aws_account_id.dkr.ecr.$<http://region.amazonaws.com/$ecr_repo_name:latest|region.amazonaws.com/$ecr_repo_name:latest>
6. Update Prefect Work Pool Configuration: Use your custom ECR image in the Prefect work pool configuration.
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yaml
work_pool:
  name: nice-workpool
  type: ecs
  job_variables:
    image: "$aws_account_id.dkr.ecr.$<http://region.amazonaws.com/$ecr_repo_name:latest|region.amazonaws.com/$ecr_repo_name:latest>"
    configure_cloudwatch_logs: true
    cloudwatch_logs_options:
      awslogs-group: "your-log-group"
      awslogs-region: "your-region"
      awslogs-stream-prefix: "ecs"
    env:
      not_secret_secret: '{{ prefect.blocks.secret.not-so-secret-1 }}'
      not_secret_secret_2: '{{ prefect.blocks.secret.not-so-secret-2 }}'
7. Redeploy Prefect Work Pool: Apply this configuration to set up your work pool with the new custom Docker image.
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sh
prefect work-pool create --name nice-workpool --type ecs --job-variables-from-file prefect.yaml
8. Run and Verify: Deploy and run your Prefect tasks using the new work pool and verify that they work as expected, including checking CloudWatch logs. For additional resources: - Amazon ECR Documentation - Prefect Docker Images This should ensure all your dependencies are neatly packaged and deployed via ECR, providing a seamless run for your Prefect flows. Let me know if you need further assistance!
a
@Marvin If it's a push workpool on aws ecs what does the entrypoint need to be?
m
For a push-based work pool on AWS ECS, the entrypoint for the Docker container should be designed to start the Prefect worker or agent properly. Here's the specific guidance: 1. Default Entrypoint: The default entrypoint for Prefect images is usually configured to start the agent or worker required to execute flows. For ECS and push-based work pools, you typically don't need to modify the default entrypoint. 2. Custom Entrypoint: If you are customizing the Docker image, ensure that the entrypoint starts the Prefect worker. The default command for an ECS worker in the context of Prefect would look something like this:
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dockerfile
   # Using Prefect's official Docker image as the base
   FROM prefecthq/prefect:latest-python3.10

   # Install necessary pip packages
   RUN pip install sagemaker boto3

   # Define the entrypoint
   ENTRYPOINT ["prefect", "worker", "start", "--pool", "my-ecs-pool"]
3. Entrypoint in Task Definition: When creating the ECS Task Definition, you can specify the entrypoint if needed. This, however, is often not necessary if the entrypoint is correctly set in the Docker image. Here’s an example of how your 
prefect.yaml
might look when deploying using this custom image:
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yaml
work_pool:
  name: nice-workpool
  type: ecs
  job_variables:
    image: "<your_aws_account_id>.dkr.ecr.<your_region>.<http://amazonaws.com/<your_ecr_repo_name>:latest|amazonaws.com/<your_ecr_repo_name>:latest>"
    configure_cloudwatch_logs: true
    cloudwatch_logs_options:
      awslogs-group: "your-log-group"
      awslogs-region: "your-region"
      awslogs-stream-prefix: "ecs"
    env:
      PREFECT_API_URL: "<https://api.prefect.io>"
      PREFECT_API_KEY: "your-prefect-api-key"
      PREFECT_LOGGING_LEVEL: "INFO"
      not_secret_secret: '{{ prefect.blocks.secret.not-so-secret-1 }}'
      not_secret_secret_2: '{{ prefect.blocks.secret.not-so-secret-2 }}'
      EXTRA_PIP_PACKAGES: "sagemaker boto3"
4. AWS ECS Console: Ensure the ECS task definition within the AWS Management Console reflects the correct settings, without a need to override the entrypoint unless specific scenarios dictate. By following these steps, you should be able to configure the entrypoint correctly for your Prefect ECS work pool to ensure seamless task processing. If you encounter issues with the entrypoint or task execution, make sure to review the ECS task logs in CloudWatch for debugging. Refer to: - Prefect AWS ECS Integration Guide - Prefect Push Work Pools Guide Feel free to reach out for more specific configurations or troubleshooting steps!
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